Llama-3.1-8B-DALv0.1
/
venv
/lib
/python3.12
/site-packages
/transformers
/models
/chameleon
/modeling_chameleon.py
# coding=utf-8 | |
# Copyright 2024 Meta Inc. and The HuggingFace Inc. team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""PyTorch Chameleon model.""" | |
import math | |
from functools import cached_property | |
from typing import Optional, Tuple, Union | |
import torch | |
import torch.nn.functional as F | |
import torch.utils.checkpoint | |
from torch import nn | |
from torch.nn import CrossEntropyLoss | |
from ...activations import ACT2FN | |
from ...cache_utils import Cache, StaticCache | |
from ...modeling_attn_mask_utils import AttentionMaskConverter | |
from ...modeling_flash_attention_utils import _flash_attention_forward | |
from ...modeling_outputs import ( | |
BaseModelOutputWithPast, | |
CausalLMOutputWithPast, | |
) | |
from ...modeling_utils import PreTrainedModel | |
from ...pytorch_utils import ALL_LAYERNORM_LAYERS | |
from ...utils import ( | |
add_code_sample_docstrings, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
is_flash_attn_2_available, | |
is_flash_attn_greater_or_equal_2_10, | |
logging, | |
replace_return_docstrings, | |
) | |
from .configuration_chameleon import ChameleonConfig, ChameleonVQVAEConfig | |
if is_flash_attn_2_available(): | |
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa | |
# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position | |
def _prepare_4d_causal_attention_mask_with_cache_position( | |
attention_mask: torch.Tensor, | |
sequence_length: int, | |
target_length: int, | |
dtype: torch.dtype, | |
device: torch.device, | |
min_dtype: float, | |
cache_position: torch.Tensor, | |
batch_size: int, | |
): | |
""" | |
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape | |
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. | |
Args: | |
attention_mask (`torch.Tensor`): | |
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. | |
sequence_length (`int`): | |
The sequence length being processed. | |
target_length (`int`): | |
The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. | |
dtype (`torch.dtype`): | |
The dtype to use for the 4D attention mask. | |
device (`torch.device`): | |
The device to plcae the 4D attention mask on. | |
min_dtype (`float`): | |
The minimum value representable with the dtype `dtype`. | |
cache_position (`torch.Tensor`): | |
Indices depicting the position of the input sequence tokens in the sequence. | |
batch_size (`torch.Tensor`): | |
Batch size. | |
""" | |
if attention_mask is not None and attention_mask.dim() == 4: | |
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. | |
causal_mask = attention_mask | |
else: | |
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) | |
if sequence_length != 1: | |
causal_mask = torch.triu(causal_mask, diagonal=1) | |
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) | |
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) | |
if attention_mask is not None: | |
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit | |
mask_length = attention_mask.shape[-1] | |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] | |
padding_mask = padding_mask == 0 | |
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( | |
padding_mask, min_dtype | |
) | |
return causal_mask | |
logger = logging.get_logger(__name__) | |
_CONFIG_FOR_DOC = "ChameleonConfig" | |
_CHECKPOINT_FOR_DOC = "meta/chameleon-7b" | |
_EXPECTED_OUTPUT_SHAPE = [1, 7, 4096] | |
_SEQ_CLASS_EXPECTED_LOSS = 1.03 | |
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'" | |
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Chameleon | |
class ChameleonRMSNorm(nn.Module): | |
def __init__(self, hidden_size, eps=1e-6): | |
""" | |
ChameleonRMSNorm is equivalent to T5LayerNorm | |
""" | |
super().__init__() | |
self.weight = nn.Parameter(torch.ones(hidden_size)) | |
self.variance_epsilon = eps | |
def forward(self, hidden_states): | |
input_dtype = hidden_states.dtype | |
hidden_states = hidden_states.to(torch.float32) | |
variance = hidden_states.pow(2).mean(-1, keepdim=True) | |
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) | |
return self.weight * hidden_states.to(input_dtype) | |
def extra_repr(self): | |
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" | |
ALL_LAYERNORM_LAYERS.append(ChameleonRMSNorm) | |
# copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Chameleon | |
# TODO(joao): add me back asap :) | |
class ChameleonRotaryEmbedding(nn.Module): | |
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): | |
super().__init__() | |
self.scaling_factor = scaling_factor | |
self.dim = dim | |
self.max_position_embeddings = max_position_embeddings | |
self.base = base | |
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) | |
self.register_buffer("inv_freq", inv_freq, persistent=False) | |
# For BC we register cos and sin cached | |
self.max_seq_len_cached = max_position_embeddings | |
def forward(self, x, position_ids): | |
# x: [bs, num_attention_heads, seq_len, head_size] | |
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) | |
position_ids_expanded = position_ids[:, None, :].float() | |
# Force float32 since bfloat16 loses precision on long contexts | |
# See https://github.com/huggingface/transformers/pull/29285 | |
device_type = x.device.type | |
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" | |
with torch.autocast(device_type=device_type, enabled=False): | |
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) | |
emb = torch.cat((freqs, freqs), dim=-1) | |
cos = emb.cos() | |
sin = emb.sin() | |
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) | |
# copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Chameleon | |
# TODO(joao): add me back asap :) | |
class ChameleonLinearScalingRotaryEmbedding(ChameleonRotaryEmbedding): | |
"""ChameleonRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" | |
def forward(self, x, position_ids): | |
# difference to the original RoPE: a scaling factor is aplied to the position ids | |
position_ids = position_ids.float() / self.scaling_factor | |
cos, sin = super().forward(x, position_ids) | |
return cos, sin | |
# copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Chameleon | |
# TODO(joao): add me back asap :) | |
class ChameleonDynamicNTKScalingRotaryEmbedding(ChameleonRotaryEmbedding): | |
"""ChameleonRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" | |
def forward(self, x, position_ids): | |
# difference to the original RoPE: inv_freq is recomputed when the sequence length > original length | |
seq_len = torch.max(position_ids) + 1 | |
if seq_len > self.max_position_embeddings: | |
base = self.base * ( | |
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) | |
) ** (self.dim / (self.dim - 2)) | |
inv_freq = 1.0 / ( | |
base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim) | |
) | |
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: this may break with compilation | |
cos, sin = super().forward(x, position_ids) | |
return cos, sin | |
# Copied from transformers.models.llama.modeling_llama.rotate_half | |
def rotate_half(x): | |
"""Rotates half the hidden dims of the input.""" | |
x1 = x[..., : x.shape[-1] // 2] | |
x2 = x[..., x.shape[-1] // 2 :] | |
return torch.cat((-x2, x1), dim=-1) | |
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb | |
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): | |
"""Applies Rotary Position Embedding to the query and key tensors. | |
Args: | |
q (`torch.Tensor`): The query tensor. | |
k (`torch.Tensor`): The key tensor. | |
cos (`torch.Tensor`): The cosine part of the rotary embedding. | |
sin (`torch.Tensor`): The sine part of the rotary embedding. | |
position_ids (`torch.Tensor`, *optional*): | |
Deprecated and unused. | |
unsqueeze_dim (`int`, *optional*, defaults to 1): | |
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and | |
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note | |
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and | |
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes | |
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have | |
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. | |
Returns: | |
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. | |
""" | |
cos = cos.unsqueeze(unsqueeze_dim) | |
sin = sin.unsqueeze(unsqueeze_dim) | |
q_embed = (q * cos) + (rotate_half(q) * sin) | |
k_embed = (k * cos) + (rotate_half(k) * sin) | |
return q_embed, k_embed | |
# Copied from transformers.models.llama.modeling_llama.LlamaMLP with Llama->Chameleon | |
class ChameleonMLP(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.config = config | |
self.hidden_size = config.hidden_size | |
self.intermediate_size = config.intermediate_size | |
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) | |
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) | |
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) | |
self.act_fn = ACT2FN[config.hidden_act] | |
# Ignore copy | |
def forward(self, x): | |
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) | |
return down_proj | |
class ChameleonLayerNorm(nn.LayerNorm): | |
""" | |
LayerNorm but computes stats only over the last dim because Chameleon applies gamma and beta | |
from each shard separately to each head, instead of reducing. We can apply each head's own | |
gamma/beta by repeat-interleaving weights from each shard, but the stats have to be computed | |
in the last dimension. This module applies gamma/beta manually to fulfill this requirement. | |
""" | |
def __init__(self, hidden_size, *args, **kwargs): | |
super().__init__(hidden_size, *args, **kwargs) | |
self.normalized_shape = (hidden_size[-1],) | |
def forward(self, hidden_states): | |
hidden_states = F.layer_norm(hidden_states, self.normalized_shape, None, None, eps=1e-5) | |
hidden_states = hidden_states * self.weight + self.bias | |
return hidden_states | |
# Copied from transformers.models.llama.modeling_llama.repeat_kv | |
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: | |
""" | |
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, | |
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) | |
""" | |
batch, num_key_value_heads, slen, head_dim = hidden_states.shape | |
if n_rep == 1: | |
return hidden_states | |
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) | |
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) | |
class ChameleonAttention(nn.Module): | |
"""Multi-headed attention from 'Attention Is All You Need' paper""" | |
def __init__(self, config: ChameleonConfig, layer_idx: Optional[int] = None): | |
super().__init__() | |
self.config = config | |
self.layer_idx = layer_idx | |
if layer_idx is None: | |
logger.warning_once( | |
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " | |
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " | |
"when creating this class." | |
) | |
self.attention_dropout = config.attention_dropout | |
self.hidden_size = config.hidden_size | |
self.num_heads = config.num_attention_heads | |
self.head_dim = self.hidden_size // self.num_heads | |
self.num_key_value_heads = config.num_key_value_heads | |
self.num_key_value_groups = self.num_heads // self.num_key_value_heads | |
self.max_position_embeddings = config.max_position_embeddings | |
self.rope_theta = config.rope_theta | |
self.is_causal = True | |
self.model_parallel_size = config.model_parallel_size | |
if (self.head_dim * self.num_heads) != self.hidden_size: | |
raise ValueError( | |
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" | |
f" and `num_heads`: {self.num_heads})." | |
) | |
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias) | |
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) | |
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) | |
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias) | |
self.q_norm = ChameleonLayerNorm((self.num_heads, self.head_dim)) | |
self.k_norm = ChameleonLayerNorm((self.num_key_value_heads, self.head_dim)) | |
self._init_rope() | |
# copied from transformers.models.llama.modeling_llama.LlamaAttention._init_rope with Llama->Chameleon | |
# TODO(joao): add me back asap :) | |
def _init_rope(self): | |
if self.config.rope_scaling is None: | |
self.rotary_emb = ChameleonRotaryEmbedding( | |
self.head_dim, | |
max_position_embeddings=self.max_position_embeddings, | |
base=self.rope_theta, | |
) | |
else: | |
scaling_type = self.config.rope_scaling["type"] | |
scaling_factor = self.config.rope_scaling["factor"] | |
if scaling_type == "linear": | |
self.rotary_emb = ChameleonLinearScalingRotaryEmbedding( | |
self.head_dim, | |
max_position_embeddings=self.max_position_embeddings, | |
scaling_factor=scaling_factor, | |
base=self.rope_theta, | |
) | |
elif scaling_type == "dynamic": | |
self.rotary_emb = ChameleonDynamicNTKScalingRotaryEmbedding( | |
self.head_dim, | |
max_position_embeddings=self.max_position_embeddings, | |
scaling_factor=scaling_factor, | |
base=self.rope_theta, | |
) | |
else: | |
raise ValueError(f"Unknown RoPE scaling type {scaling_type}") | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
past_key_value: Optional[Cache] = None, | |
output_attentions: bool = False, | |
use_cache: bool = False, | |
cache_position: Optional[torch.LongTensor] = None, | |
**kwargs, | |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | |
bsz, q_len, _ = hidden_states.size() | |
query_states = self.q_proj(hidden_states) | |
key_states = self.k_proj(hidden_states) | |
value_states = self.v_proj(hidden_states) | |
query_states = query_states.reshape(-1, self.num_heads, self.head_dim) | |
query_states = self.q_norm(query_states) | |
key_states = key_states.reshape(-1, self.num_key_value_heads, self.head_dim) | |
key_states = self.k_norm(key_states) | |
query_states = query_states.reshape(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) | |
key_states = key_states.reshape(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) | |
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) | |
cos, sin = self.rotary_emb(value_states, position_ids) | |
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) | |
if past_key_value is not None: | |
# sin and cos are specific to RoPE models; position_ids needed for the static cache | |
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} | |
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) | |
key_states = repeat_kv(key_states, self.num_key_value_groups) | |
value_states = repeat_kv(value_states, self.num_key_value_groups) | |
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) | |
if attention_mask is not None: # no matter the length, we just slice it | |
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] | |
attn_weights = attn_weights + causal_mask | |
# upcast attention to fp32 | |
attn_weights = nn.functional.softmax(attn_weights, dim=-1).to(query_states.dtype) | |
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) | |
attn_output = torch.matmul(attn_weights, value_states) | |
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): | |
raise ValueError( | |
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" | |
f" {attn_output.size()}" | |
) | |
attn_output = attn_output.transpose(1, 2).contiguous() | |
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) | |
attn_output = self.o_proj(attn_output) | |
if not output_attentions: | |
attn_weights = None | |
return attn_output, attn_weights, past_key_value | |
# copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->Chameleon | |
# TODO(joao): add me back asap :) | |
class ChameleonFlashAttention2(ChameleonAttention): | |
""" | |
Chameleon flash attention module. This module inherits from `ChameleonAttention` as the weights of the module stays | |
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of | |
flash attention and deal with padding tokens in case the input contains any of them. | |
""" | |
def __init__(self, *args, **kwargs): | |
super().__init__(*args, **kwargs) | |
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. | |
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. | |
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). | |
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() | |
# Ignore copy | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.LongTensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
past_key_value: Optional[Cache] = None, | |
output_attentions: bool = False, | |
use_cache: bool = False, | |
cache_position: Optional[torch.LongTensor] = None, | |
**kwargs, | |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | |
if isinstance(past_key_value, StaticCache): | |
raise ValueError( | |
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` " | |
"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers" | |
) | |
output_attentions = False | |
bsz, q_len, _ = hidden_states.size() | |
query_states = self.q_proj(hidden_states) | |
key_states = self.k_proj(hidden_states) | |
value_states = self.v_proj(hidden_states) | |
query_states = query_states.reshape(-1, self.num_heads, self.head_dim) | |
query_states = self.q_norm(query_states) | |
key_states = key_states.reshape(-1, self.num_key_value_heads, self.head_dim) | |
key_states = self.k_norm(key_states) | |
# Flash attention requires the input to have the shape | |
# batch_size x seq_length x head_dim x hidden_dim | |
# therefore we just need to keep the original shape | |
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) | |
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) | |
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) | |
cos, sin = self.rotary_emb(value_states, position_ids) | |
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) | |
if past_key_value is not None: | |
# sin and cos are specific to RoPE models; position_ids needed for the static cache | |
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} | |
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) | |
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. | |
# We would need to refactor the KV cache to be able to avoid many of these transpose/reshape/view. | |
query_states = query_states.transpose(1, 2) | |
key_states = key_states.transpose(1, 2) | |
value_states = value_states.transpose(1, 2) | |
dropout_rate = self.attention_dropout if self.training else 0.0 | |
# In PEFT, usually we cast the layer norms in float32 for training stability reasons | |
# therefore the input hidden states gets silently casted in float32. Hence, we need | |
# cast them back in the correct dtype just to be sure everything works as expected. | |
# This might slowdown training & inference so it is recommended to not cast the LayerNorms | |
# in fp32. (ChameleonRMSNorm handles it correctly) | |
input_dtype = query_states.dtype | |
if input_dtype == torch.float32: | |
if torch.is_autocast_enabled(): | |
target_dtype = torch.get_autocast_gpu_dtype() | |
# Handle the case where the model is quantized | |
elif hasattr(self.config, "_pre_quantization_dtype"): | |
target_dtype = self.config._pre_quantization_dtype | |
else: | |
target_dtype = self.q_proj.weight.dtype | |
logger.warning_once( | |
f"The input hidden states seems to be silently casted in float32, this might be related to" | |
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" | |
f" {target_dtype}." | |
) | |
query_states = query_states.to(target_dtype) | |
key_states = key_states.to(target_dtype) | |
value_states = value_states.to(target_dtype) | |
attn_output = _flash_attention_forward( | |
query_states, | |
key_states, | |
value_states, | |
attention_mask, | |
q_len, | |
dropout=dropout_rate, | |
sliding_window=getattr(self, "sliding_window", None), | |
use_top_left_mask=self._flash_attn_uses_top_left_mask, | |
is_causal=self.is_causal, | |
) | |
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() | |
attn_output = self.o_proj(attn_output) | |
if not output_attentions: | |
attn_weights = None | |
return attn_output, attn_weights, past_key_value | |
class ChameleonSdpaAttention(ChameleonAttention): | |
""" | |
Chameleon attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from | |
`ChameleonAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to | |
SDPA API. | |
""" | |
# Adapted from ChameleonAttention.forward | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
past_key_value: Optional[Cache] = None, | |
output_attentions: bool = False, | |
use_cache: bool = False, | |
cache_position: Optional[torch.LongTensor] = None, | |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | |
if output_attentions: | |
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. | |
logger.warning_once( | |
"ChameleonModel is using ChameleonSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " | |
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' | |
) | |
return super().forward( | |
hidden_states=hidden_states, | |
attention_mask=attention_mask, | |
position_ids=position_ids, | |
past_key_value=past_key_value, | |
output_attentions=output_attentions, | |
use_cache=use_cache, | |
cache_position=cache_position, | |
) | |
bsz, q_len, _ = hidden_states.size() | |
query_states = self.q_proj(hidden_states) | |
key_states = self.k_proj(hidden_states) | |
value_states = self.v_proj(hidden_states) | |
query_states = query_states.reshape(-1, self.num_heads, self.head_dim) | |
query_states = self.q_norm(query_states) | |
key_states = key_states.reshape(-1, self.num_key_value_heads, self.head_dim) | |
key_states = self.k_norm(key_states) | |
query_states = query_states.reshape(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) | |
key_states = key_states.reshape(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) | |
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) | |
cos, sin = self.rotary_emb(value_states, position_ids) | |
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None) | |
if past_key_value is not None: | |
# sin and cos are specific to RoPE models; position_ids needed for the static cache | |
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} | |
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) | |
key_states = repeat_kv(key_states, self.num_key_value_groups) | |
value_states = repeat_kv(value_states, self.num_key_value_groups) | |
causal_mask = attention_mask | |
if attention_mask is not None and cache_position is not None: | |
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] | |
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, | |
# Reference: https://github.com/pytorch/pytorch/issues/112577. | |
if query_states.device.type == "cuda" and causal_mask is not None: | |
query_states = query_states.contiguous() | |
key_states = key_states.contiguous() | |
value_states = value_states.contiguous() | |
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment | |
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. | |
is_causal = True if causal_mask is None and q_len > 1 else False | |
attn_output = torch.nn.functional.scaled_dot_product_attention( | |
query_states, | |
key_states, | |
value_states, | |
attn_mask=causal_mask, | |
dropout_p=self.attention_dropout if self.training else 0.0, | |
is_causal=is_causal, | |
) | |
attn_output = attn_output.transpose(1, 2).contiguous() | |
attn_output = attn_output.view(bsz, q_len, self.hidden_size) | |
attn_output = self.o_proj(attn_output) | |
return attn_output, None, past_key_value | |
CHAMELEON_ATTENTION_CLASSES = { | |
"eager": ChameleonAttention, | |
"flash_attention_2": ChameleonFlashAttention2, | |
"sdpa": ChameleonSdpaAttention, | |
} | |
# copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with Llama->Chameleon, LLAMA->CHAMELEON | |
# TODO(joao): add me back asap :) | |
class ChameleonDecoderLayer(nn.Module): | |
def __init__(self, config: ChameleonConfig, layer_idx: int): | |
super().__init__() | |
self.hidden_size = config.hidden_size | |
self.self_attn = CHAMELEON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) | |
self.mlp = ChameleonMLP(config) | |
self.input_layernorm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps) | |
self.post_attention_layernorm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps) | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
past_key_value: Optional[Cache] = None, | |
output_attentions: Optional[bool] = False, | |
use_cache: Optional[bool] = False, | |
cache_position: Optional[torch.LongTensor] = None, | |
**kwargs, | |
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: | |
""" | |
Args: | |
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` | |
attention_mask (`torch.FloatTensor`, *optional*): | |
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, | |
query_sequence_length, key_sequence_length)` if default attention is used. | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under | |
returned tensors for more detail. | |
use_cache (`bool`, *optional*): | |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding | |
(see `past_key_values`). | |
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states | |
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): | |
Indices depicting the position of the input sequence tokens in the sequence | |
kwargs (`dict`, *optional*): | |
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code | |
into the model | |
""" | |
residual = hidden_states | |
hidden_states = self.input_layernorm(hidden_states) | |
# Self Attention | |
hidden_states, self_attn_weights, present_key_value = self.self_attn( | |
hidden_states=hidden_states, | |
attention_mask=attention_mask, | |
position_ids=position_ids, | |
past_key_value=past_key_value, | |
output_attentions=output_attentions, | |
use_cache=use_cache, | |
cache_position=cache_position, | |
**kwargs, | |
) | |
hidden_states = residual + hidden_states | |
# Fully Connected | |
residual = hidden_states | |
hidden_states = self.post_attention_layernorm(hidden_states) | |
hidden_states = self.mlp(hidden_states) | |
hidden_states = residual + hidden_states | |
outputs = (hidden_states,) | |
if output_attentions: | |
outputs += (self_attn_weights,) | |
if use_cache: | |
outputs += (present_key_value,) | |
return outputs | |
class ChameleonSwinDecoderLayer(nn.Module): | |
def __init__(self, config: ChameleonConfig, layer_idx: int): | |
super().__init__() | |
self.hidden_size = config.hidden_size | |
self.self_attn = CHAMELEON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) | |
self.mlp = ChameleonMLP(config) | |
self.input_layernorm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps) | |
self.post_attention_layernorm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps) | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
past_key_value: Optional[Cache] = None, | |
output_attentions: Optional[bool] = False, | |
use_cache: Optional[bool] = False, | |
cache_position: Optional[torch.LongTensor] = None, | |
**kwargs, | |
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: | |
""" | |
Args: | |
hidden_states (`torch.FloatTensor`): | |
input to the layer of shape `(batch, seq_len, embed_dim)` | |
attention_mask (`torch.FloatTensor`, *optional*): | |
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, | |
query_sequence_length, key_sequence_length)` if default attention is used. | |
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Indices of positions of each input sequence tokens in the position embeddings | |
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under | |
returned tensors for more detail. | |
use_cache (`bool`, *optional*): | |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding | |
(see `past_key_values`). | |
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): | |
Indices depicting the position of the input sequence tokens in the sequence. | |
""" | |
residual = hidden_states | |
# Self Attention | |
hidden_states, self_attn_weights, present_key_value = self.self_attn( | |
hidden_states=hidden_states, | |
attention_mask=attention_mask, | |
position_ids=position_ids, | |
past_key_value=past_key_value, | |
output_attentions=output_attentions, | |
use_cache=use_cache, | |
cache_position=cache_position, | |
**kwargs, | |
) | |
hidden_states = self.input_layernorm(hidden_states) | |
hidden_states = residual + hidden_states | |
# Fully Connected | |
residual = hidden_states | |
hidden_states = self.mlp(hidden_states) | |
hidden_states = self.post_attention_layernorm(hidden_states) | |
hidden_states = residual + hidden_states | |
outputs = (hidden_states,) | |
if output_attentions: | |
outputs += (self_attn_weights,) | |
if use_cache: | |
outputs += (present_key_value,) | |
return outputs | |
class ChameleonVQVAEVectorQuantizer(nn.Module): | |
""" | |
A module for vector quantization using learned embedding vectors. | |
This module implements the quantization process similar to te one described in | |
the VQ-VAE (Vector Quantized Variational AutoEncoder) paper. It quantizes continuous | |
input vectors into discrete codebook vectors, which are learned during training. | |
Current implementation improves over previous ones by avoiding costly matrix multiplications | |
and allowing for post-hoc remapping of indices. | |
""" | |
def __init__(self, config): | |
super().__init__() | |
self.num_embeddings = config.num_embeddings | |
self.embedding_dim = config.embed_dim | |
self.beta = getattr(config, "beta", 0.25) | |
self.embedding = nn.Embedding(self.num_embeddings, self.embedding_dim) | |
self.re_embed = self.num_embeddings | |
def forward(self, hidden_state: torch.Tensor): | |
hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous() | |
hidden_state_flattened = hidden_state.view(-1, self.embedding_dim) | |
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z | |
distances = ( | |
torch.sum(hidden_state_flattened**2, dim=1, keepdim=True) | |
+ torch.sum(self.embedding.weight**2, dim=1) | |
- 2 * torch.einsum("bd,dn->bn", hidden_state_flattened, self.embedding.weight.transpose(0, 1)) | |
) | |
min_encoding_indices = torch.argmin(distances, dim=1) | |
hidden_state_quant = self.embedding(min_encoding_indices).view(hidden_state.shape) | |
# compute loss for embedding | |
loss = torch.mean((hidden_state_quant.detach() - hidden_state) ** 2) + self.beta * torch.mean( | |
(hidden_state_quant - hidden_state.detach()) ** 2 | |
) | |
# preserve gradients | |
hidden_state_quant = hidden_state + (hidden_state_quant - hidden_state).detach() | |
# reshape back to match original input shape | |
hidden_state_quant = hidden_state_quant.permute(0, 3, 1, 2).contiguous() | |
return hidden_state_quant, loss, min_encoding_indices | |
class ChameleonVQVAEEncoderConvDownsample(nn.Module): | |
def __init__(self, in_channels): | |
super().__init__() | |
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) | |
def forward(self, hidden_states): | |
# no asymmetric padding in torch conv, must do it ourselves | |
hidden_states = F.pad(hidden_states, pad=(0, 1, 0, 1), mode="constant", value=0) | |
hidden_states = self.conv(hidden_states) | |
return hidden_states | |
class ChameleonVQVAEEncoderResnetBlock(nn.Module): | |
def __init__( | |
self, | |
config, | |
in_channels, | |
out_channels=None, | |
conv_shortcut=False, | |
): | |
super().__init__() | |
self.in_channels = in_channels | |
self.out_channels = in_channels if out_channels is None else out_channels | |
self.use_conv_shortcut = conv_shortcut | |
self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) | |
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True) | |
self.dropout = torch.nn.Dropout(config.dropout) | |
self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
if self.in_channels != self.out_channels: | |
if self.use_conv_shortcut: | |
self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
else: | |
self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) | |
def forward(self, hidden_states): | |
residual = hidden_states | |
hidden_states = self.norm1(hidden_states) | |
hidden_states *= torch.sigmoid(hidden_states) | |
hidden_states = self.conv1(hidden_states) | |
hidden_states = self.norm2(hidden_states) | |
hidden_states *= torch.sigmoid(hidden_states) | |
hidden_states = self.dropout(hidden_states) | |
hidden_states = self.conv2(hidden_states) | |
if self.in_channels != self.out_channels: | |
if self.use_conv_shortcut: | |
residual = self.conv_shortcut(residual) | |
else: | |
residual = self.nin_shortcut(residual) | |
return residual + hidden_states | |
class ChameleonVQVAEEncoderAttnBlock(nn.Module): | |
def __init__(self, in_channels): | |
super().__init__() | |
self.in_channels = in_channels | |
self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) | |
self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) | |
self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) | |
self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) | |
self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) | |
def forward(self, hidden_states): | |
residual = hidden_states | |
hidden_states = self.norm(hidden_states) | |
query_states = self.q(hidden_states) | |
key_states = self.k(hidden_states) | |
value_states = self.v(hidden_states) | |
# compute attention | |
batch_size, channels, height, width = query_states.shape | |
query_states = query_states.reshape(batch_size, channels, height * width).permute(0, 2, 1) | |
key_states = key_states.reshape(batch_size, channels, height * width) | |
attn_weights = torch.bmm(query_states, key_states) | |
attn_weights = attn_weights * (int(channels) ** (-0.5)) | |
attn_weights = F.softmax(attn_weights, dim=2) | |
# attend to values | |
value_states = value_states.reshape(batch_size, channels, height * width) | |
attn_weights = attn_weights.permute(0, 2, 1) | |
attn_output = torch.bmm(value_states, attn_weights).reshape(batch_size, channels, height, width) | |
attn_output = self.proj_out(attn_output) | |
return residual + attn_output | |
class ChameleonVQVAEEncoder(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.num_resolutions = len(config.channel_multiplier) | |
self.num_res_blocks = config.num_res_blocks | |
base_channels = config.base_channels | |
resolution = config.resolution | |
in_channels = config.in_channels | |
double_latent = config.double_latent | |
latent_channels = config.latent_channels | |
channel_multiplier = config.channel_multiplier | |
self.conv_in = torch.nn.Conv2d(in_channels, base_channels, kernel_size=3, stride=1, padding=1) | |
curr_res = resolution | |
in_channel_multiplier = (1,) + tuple(channel_multiplier) | |
self.in_channel_multiplier = in_channel_multiplier | |
self.down = nn.ModuleList() | |
for i_level in range(self.num_resolutions): | |
block = nn.ModuleList() | |
attn = nn.ModuleList() | |
block_in = base_channels * in_channel_multiplier[i_level] | |
block_out = base_channels * channel_multiplier[i_level] | |
for i_block in range(self.num_res_blocks): | |
block.append( | |
ChameleonVQVAEEncoderResnetBlock( | |
config=config, | |
in_channels=block_in, | |
out_channels=block_out, | |
) | |
) | |
block_in = block_out | |
if ( | |
config.attn_resolutions is not None | |
and curr_res in config.attn_resolutions | |
and config.attn_type == "vanilla" | |
): | |
attn.append(ChameleonVQVAEEncoderAttnBlock(block_in)) | |
down = nn.Module() | |
down.block = block | |
down.attn = attn | |
if i_level != self.num_resolutions - 1: | |
down.downsample = ChameleonVQVAEEncoderConvDownsample(block_in) | |
curr_res = curr_res // 2 | |
self.down.append(down) | |
self.mid = nn.Module() | |
self.mid.block_1 = ChameleonVQVAEEncoderResnetBlock( | |
config=config, | |
in_channels=block_in, | |
out_channels=block_in, | |
) | |
self.mid.attn_1 = ChameleonVQVAEEncoderAttnBlock(block_in) if config.attn_type == "vanilla" else nn.Identity() | |
self.mid.block_2 = ChameleonVQVAEEncoderResnetBlock( | |
config=config, | |
in_channels=block_in, | |
out_channels=block_in, | |
) | |
self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) | |
self.conv_out = torch.nn.Conv2d( | |
block_in, | |
2 * latent_channels if double_latent else latent_channels, | |
kernel_size=3, | |
stride=1, | |
padding=1, | |
) | |
def forward(self, pixel_values: torch.LongTensor): | |
# downsampling | |
hidden_states = [self.conv_in(pixel_values)] | |
for i_level in range(self.num_resolutions): | |
for i_block in range(self.num_res_blocks): | |
hidden_state = self.down[i_level].block[i_block]( | |
hidden_states[-1], | |
) | |
if len(self.down[i_level].attn) > 0: | |
hidden_state = self.down[i_level].attn[i_block](hidden_state) | |
hidden_states.append(hidden_state) | |
if i_level != self.num_resolutions - 1: | |
hidden_states.append(self.down[i_level].downsample(hidden_states[-1])) | |
# middle | |
last_hidden_state = hidden_states[-1] | |
last_hidden_state = self.mid.block_1(last_hidden_state) | |
last_hidden_state = self.mid.attn_1(last_hidden_state) | |
last_hidden_state = self.mid.block_2(last_hidden_state) | |
# end | |
last_hidden_state = self.norm_out(last_hidden_state) | |
last_hidden_state *= torch.sigmoid(last_hidden_state) | |
last_hidden_state = self.conv_out(last_hidden_state) | |
return last_hidden_state | |
CHAMELEON_VQ_START_DOCSTRING = r""" | |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the | |
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads | |
etc.) | |
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | |
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage | |
and behavior. | |
Parameters: | |
config ([`ChameleonVQVAEConfig`]): | |
Model configuration class with all the parameters of the model. Initializing with a config file does not | |
load the weights associated with the model, only the configuration. Check out the | |
[`~PreTrainedModel.from_pretrained`] method to load the model weights. | |
""" | |
class ChameleonVQVAE(PreTrainedModel): | |
config_class = ChameleonVQVAEConfig | |
_no_split_modules = ["ChameleonVQVAEVectorQuantizer"] | |
def _init_weights(self, module): | |
std = self.config.initializer_range | |
if isinstance(module, nn.Embedding): | |
module.weight.data.normal_(mean=0.0, std=std) | |
elif isinstance(module, nn.GroupNorm): | |
module.bias.data.zero_() | |
module.weight.data.fill_(1.0) | |
elif isinstance(module, (nn.Linear, nn.Conv2d)): | |
module.weight.data.normal_(mean=0.0, std=std) | |
if module.bias is not None: | |
module.bias.data.zero_() | |
def __init__(self, config: ChameleonVQVAEConfig): | |
super().__init__(config) | |
self.encoder = ChameleonVQVAEEncoder(config) | |
self.quantize = ChameleonVQVAEVectorQuantizer(config) | |
self.quant_conv = torch.nn.Conv2d(config.latent_channels, config.embed_dim, 1) | |
self.post_quant_conv = torch.nn.Conv2d(config.embed_dim, config.latent_channels, 1) | |
self.eval() # Chameleon's VQ model is frozen | |
def encode(self, pixel_values: torch.LongTensor): | |
hidden_states = self.encoder(pixel_values) | |
hidden_states = self.quant_conv(hidden_states) | |
quant, emb_loss, indices = self.quantize(hidden_states) | |
return quant, emb_loss, indices | |
class ChameleonImageVocabularyMapping: | |
""" | |
A class for mapping discrete image tokens from VQGAN to BPE tokens. | |
""" | |
def __init__(self, vocab_map): | |
self.vocab_map = vocab_map | |
self.image_token_id = vocab_map.get("<image>") | |
def val2name(self): | |
return {v: k for k, v in self.vocab_map.items()} | |
def image_tokens(self): | |
return sorted([val for name, val in self.vocab_map.items() if name.startswith("IMGIMG")]) | |
def bpe2img(self): | |
img_tkn_chr_mapping = {chr(ord("A") + i): str(i) for i in range(10)} | |
def remap(old_name: str) -> str: | |
return "".join(img_tkn_chr_mapping.get(c, c) for c in old_name[len("IMGIMG") : -1]) | |
return {tok: int(remap(self.val2name[tok])) for tok in self.image_tokens} | |
def img2bpe(self): | |
return {v: k for k, v in self.bpe2img.items()} | |
def bpe2img_search_tensors(self): | |
return torch.tensor(sorted(self.bpe2img.keys())), torch.tensor(sorted(self.bpe2img.values())) | |
def img2bpe_mapping_tensor(self): | |
mapping = torch.zeros(max(self.img2bpe.keys()) + 1, dtype=torch.int) | |
for k, v in self.img2bpe.items(): | |
mapping[k] = v | |
return mapping | |
def convert_img2bpe(self, img_batch: torch.Tensor) -> torch.Tensor: | |
device = img_batch.device | |
img_tokens = self.img2bpe_mapping_tensor[img_batch.to("cpu")] | |
return img_tokens.to(device) | |
CHAMELEON_START_DOCSTRING = r""" | |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the | |
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads | |
etc.) | |
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | |
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage | |
and behavior. | |
Parameters: | |
config ([`ChameleonConfig`]): | |
Model configuration class with all the parameters of the model. Initializing with a config file does not | |
load the weights associated with the model, only the configuration. Check out the | |
[`~PreTrainedModel.from_pretrained`] method to load the model weights. | |
""" | |
class ChameleonPreTrainedModel(PreTrainedModel): | |
config_class = ChameleonConfig | |
base_model_prefix = "model" | |
supports_gradient_checkpointing = True | |
_no_split_modules = ["ChameleonDecoderLayer", "ChameleonSwinDecoderLayer"] | |
_skip_keys_device_placement = ["past_key_values", "causal_mask"] | |
_supports_flash_attn_2 = True | |
_supports_sdpa = True | |
_supports_quantized_cache = True | |
_supports_cache_class = True | |
_supports_static_cache = True | |
_supports_param_buffer_assignment = False | |
def _init_weights(self, module): | |
std = self.config.initializer_range | |
if isinstance(module, ChameleonVQVAE): | |
module.apply(module._init_weights) | |
elif isinstance(module, (nn.Linear, nn.Conv2d)): | |
module.weight.data.normal_(mean=0.0, std=std) | |
if module.bias is not None: | |
module.bias.data.zero_() | |
elif isinstance(module, nn.Embedding): | |
module.weight.data.normal_(mean=0.0, std=std) | |
if module.padding_idx is not None: | |
module.weight.data[module.padding_idx].zero_() | |
CHAMELEON_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): | |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide | |
it. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are input IDs?](../glossary#input-ids) | |
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)): | |
The tensors corresponding to the input images. Pixel values can be obtained using | |
[`AutoImageProcessor`]. See [`ChameleonImageProcessor.__call__`] for details. | |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see | |
`past_key_values`). | |
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] | |
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more | |
information on the default strategy. | |
- 1 indicates the head is **not masked**, | |
- 0 indicates the head is **masked**. | |
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, | |
config.n_positions - 1]`. | |
[What are position IDs?](../glossary#position-ids) | |
past_key_values (`Cache`, *optional*): | |
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention | |
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` | |
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. | |
Should always be a [`~cache_utils.Cache`] instance and the model will output the same cache instance. | |
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't | |
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` | |
of shape `(batch_size, sequence_length)`. | |
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This | |
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the | |
model's internal embedding lookup matrix. | |
use_cache (`bool`, *optional*): | |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see | |
`past_key_values`). | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned | |
tensors for more detail. | |
output_hidden_states (`bool`, *optional*): | |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for | |
more detail. | |
return_dict (`bool`, *optional*): | |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. | |
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): | |
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, | |
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer | |
the complete sequence length. | |
""" | |
class ChameleonModel(ChameleonPreTrainedModel): | |
""" | |
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`ChameleonDecoderLayer`] | |
Args: | |
config: ChameleonConfig | |
""" | |
def __init__(self, config: ChameleonConfig): | |
super().__init__(config) | |
self.padding_idx = config.pad_token_id | |
self.vocab_size = config.vocab_size | |
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) | |
self.vocabulary_mapping = ChameleonImageVocabularyMapping(config.vocabulary_map) | |
decoder_layer = ChameleonDecoderLayer if not self.config.swin_norm else ChameleonSwinDecoderLayer | |
self.layers = nn.ModuleList( | |
[decoder_layer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] | |
) | |
self.norm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps) | |
self.vqmodel = ChameleonVQVAE(config.vq_config) | |
self.gradient_checkpointing = False | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_input_embeddings(self): | |
return self.embed_tokens | |
def set_input_embeddings(self, value): | |
self.embed_tokens = value | |
def get_image_tokens(self, pixel_values: torch.FloatTensor): | |
""" | |
Tokenizes images into discrete tokens with VQGAN module. Converts | |
obtained image tokens into BPE tokens and wraps with "boi" and "eoi" | |
special tokens. | |
Args: | |
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)): | |
The tensors corresponding to the input images. | |
""" | |
batch_size = pixel_values.shape[0] | |
_, _, image_toks = self.vqmodel.encode(pixel_values) | |
bpe_toks = self.vocabulary_mapping.convert_img2bpe(image_toks) | |
bpe_toks = bpe_toks.view(batch_size, -1) | |
return bpe_toks | |
def forward( | |
self, | |
input_ids: torch.LongTensor = None, | |
pixel_values: torch.FloatTensor = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
past_key_values: Optional[Cache] = None, | |
inputs_embeds: Optional[torch.FloatTensor] = None, | |
use_cache: Optional[bool] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
cache_position: Optional[torch.LongTensor] = None, | |
) -> Union[Tuple, BaseModelOutputWithPast]: | |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions | |
output_hidden_states = ( | |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states | |
) | |
use_cache = use_cache if use_cache is not None else self.config.use_cache | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
if self.gradient_checkpointing and self.training and use_cache: | |
logger.warning_once( | |
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." | |
) | |
use_cache = False | |
if (input_ids is None) ^ (inputs_embeds is not None): | |
raise ValueError( | |
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" | |
) | |
if pixel_values is not None and inputs_embeds is not None: | |
raise ValueError( | |
"You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one" | |
) | |
if pixel_values is not None: | |
image_tokens = self.get_image_tokens(pixel_values) | |
special_image_mask = input_ids == self.vocabulary_mapping.image_token_id | |
image_tokens = image_tokens.to(input_ids.device, input_ids.dtype) | |
input_ids = input_ids.masked_scatter(special_image_mask, image_tokens) | |
if inputs_embeds is None: | |
inputs_embeds = self.embed_tokens(input_ids) | |
if cache_position is None: | |
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 | |
cache_position = torch.arange( | |
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device | |
) | |
if position_ids is None: | |
position_ids = cache_position.unsqueeze(0) | |
causal_mask = self._update_causal_mask( | |
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions | |
) | |
# embed positions | |
hidden_states = inputs_embeds | |
# decoder layers | |
all_hidden_states = () if output_hidden_states else None | |
all_self_attns = () if output_attentions else None | |
next_decoder_cache = None | |
for decoder_layer in self.layers: | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
if self.gradient_checkpointing and self.training: | |
layer_outputs = self._gradient_checkpointing_func( | |
decoder_layer.__call__, | |
hidden_states, | |
causal_mask, | |
position_ids, | |
past_key_values, | |
output_attentions, | |
use_cache, | |
cache_position, | |
) | |
else: | |
layer_outputs = decoder_layer( | |
hidden_states, | |
attention_mask=causal_mask, | |
position_ids=position_ids, | |
past_key_value=past_key_values, | |
output_attentions=output_attentions, | |
use_cache=use_cache, | |
cache_position=cache_position, | |
) | |
hidden_states = layer_outputs[0] | |
if use_cache: | |
next_decoder_cache = layer_outputs[2 if output_attentions else 1] | |
if output_attentions: | |
all_self_attns += (layer_outputs[1],) | |
hidden_states = self.norm(hidden_states) | |
# add hidden states from the last decoder layer | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
next_cache = None | |
if use_cache: | |
next_cache = next_decoder_cache | |
if not return_dict: | |
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) | |
return BaseModelOutputWithPast( | |
last_hidden_state=hidden_states, | |
past_key_values=next_cache, | |
hidden_states=all_hidden_states, | |
attentions=all_self_attns, | |
) | |
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask | |
def _update_causal_mask( | |
self, | |
attention_mask: torch.Tensor, | |
input_tensor: torch.Tensor, | |
cache_position: torch.Tensor, | |
past_key_values: Cache, | |
output_attentions: bool, | |
): | |
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static | |
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. | |
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using | |
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 | |
if self.config._attn_implementation == "flash_attention_2": | |
if attention_mask is not None and 0.0 in attention_mask: | |
return attention_mask | |
return None | |
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in | |
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail | |
# to infer the attention mask. | |
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 | |
using_static_cache = isinstance(past_key_values, StaticCache) | |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward | |
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: | |
if AttentionMaskConverter._ignore_causal_mask_sdpa( | |
attention_mask, | |
inputs_embeds=input_tensor, | |
past_key_values_length=past_seen_tokens, | |
is_training=self.training, | |
): | |
return None | |
dtype, device = input_tensor.dtype, input_tensor.device | |
min_dtype = torch.finfo(dtype).min | |
sequence_length = input_tensor.shape[1] | |
if using_static_cache: | |
target_length = past_key_values.get_max_length() | |
else: | |
target_length = ( | |
attention_mask.shape[-1] | |
if isinstance(attention_mask, torch.Tensor) | |
else past_seen_tokens + sequence_length + 1 | |
) | |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D). | |
causal_mask = _prepare_4d_causal_attention_mask_with_cache_position( | |
attention_mask, | |
sequence_length=sequence_length, | |
target_length=target_length, | |
dtype=dtype, | |
device=device, | |
min_dtype=min_dtype, | |
cache_position=cache_position, | |
batch_size=input_tensor.shape[0], | |
) | |
if ( | |
self.config._attn_implementation == "sdpa" | |
and attention_mask is not None | |
and attention_mask.device.type == "cuda" | |
and not output_attentions | |
): | |
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when | |
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. | |
# Details: https://github.com/pytorch/pytorch/issues/110213 | |
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) | |
return causal_mask | |
class ChameleonForConditionalGeneration(ChameleonPreTrainedModel): | |
_tied_weights_keys = ["lm_head.weight"] | |
def __init__(self, config): | |
super().__init__(config) | |
self.model = ChameleonModel(config) | |
self.vocab_size = config.vocab_size | |
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_input_embeddings(self): | |
return self.model.embed_tokens | |
def set_input_embeddings(self, value): | |
self.model.embed_tokens = value | |
def get_output_embeddings(self): | |
return self.lm_head | |
def set_output_embeddings(self, new_embeddings): | |
self.lm_head = new_embeddings | |
def set_decoder(self, decoder): | |
self.model = decoder | |
def get_decoder(self): | |
return self.model | |
def forward( | |
self, | |
input_ids: torch.LongTensor = None, | |
pixel_values: torch.FloatTensor = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
position_ids: Optional[torch.LongTensor] = None, | |
past_key_values: Optional[Cache] = None, | |
inputs_embeds: Optional[torch.FloatTensor] = None, | |
labels: Optional[torch.LongTensor] = None, | |
use_cache: Optional[bool] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
cache_position: Optional[torch.LongTensor] = None, | |
) -> Union[Tuple, CausalLMOutputWithPast]: | |
r""" | |
Args: | |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., | |
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored | |
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. | |
Returns: | |
Example: | |
```python | |
>>> from transformers import ChameleonProcessor, ChameleonForConditionalGeneration | |
>>> import torch | |
>>> import requests | |
>>> from PIL import Image | |
>>> model = ChameleonForConditionalGeneration.from_pretrained("facebook/chameleon-7b", torch_dtype=torch.bfloat16) | |
>>> processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b") | |
>>> prompt = "I used to know a lot about constellations when I was younger, but as I grew older, I forgot most of what I knew. These are the only two constellations that I really remember now.<image><image>I would like for you to tell me about 3 more constellations and give me a little bit of history about the constellation." | |
>>> image = Image.open(requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw) | |
>>> image_2 = Image.open(requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw) | |
>>> inputs = processor(prompt, images=[image, image_2], return_tensors="pt").to(model.device, torch.bfloat16) | |
>>> generated_ids = model.generate(**inputs, max_new_tokens=100, do_sample=False) | |
>>> processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
```""" | |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions | |
output_hidden_states = ( | |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states | |
) | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) | |
outputs = self.model( | |
input_ids=input_ids, | |
pixel_values=pixel_values, | |
attention_mask=attention_mask, | |
position_ids=position_ids, | |
past_key_values=past_key_values, | |
inputs_embeds=inputs_embeds, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
cache_position=cache_position, | |
) | |
hidden_states = outputs[0] | |
logits = self.lm_head(hidden_states) | |
logits = logits.float() | |
# Disallow image tokens which does not include special begin-image and end-image tokens | |
image_tokens = self.model.vocabulary_mapping.image_tokens | |
logits[:, :, image_tokens] = torch.finfo(logits.dtype).min | |
loss = None | |
if labels is not None: | |
# Shift so that tokens < n predict n | |
shift_logits = logits[..., :-1, :].contiguous() | |
shift_labels = labels[..., 1:].contiguous() | |
# Flatten the tokens | |
loss_fct = CrossEntropyLoss() | |
shift_logits = shift_logits.view(-1, self.config.vocab_size) | |
shift_labels = shift_labels.view(-1) | |
# Enable model parallelism | |
shift_labels = shift_labels.to(shift_logits.device) | |
loss = loss_fct(shift_logits, shift_labels) | |
if not return_dict: | |
output = (logits,) + outputs[1:] | |
return (loss,) + output if loss is not None else output | |
return CausalLMOutputWithPast( | |
loss=loss, | |
logits=logits, | |
past_key_values=outputs.past_key_values, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
def prepare_inputs_for_generation( | |
self, | |
input_ids, | |
pixel_values=None, | |
past_key_values=None, | |
attention_mask=None, | |
inputs_embeds=None, | |
cache_position=None, | |
position_ids=None, | |
use_cache=True, | |
**kwargs, | |
): | |
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens | |
# Exception 1: when passing input_embeds, input_ids may be missing entries | |
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here | |
if past_key_values is not None: | |
if inputs_embeds is not None: # Exception 1 | |
input_ids = input_ids[:, -cache_position.shape[0] :] | |
elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) | |
input_ids = input_ids[:, cache_position] | |
if attention_mask is not None and position_ids is None: | |
# create position_ids on the fly for batch generation | |
position_ids = attention_mask.long().cumsum(-1) - 1 | |
position_ids.masked_fill_(attention_mask == 0, 1) | |
if past_key_values: | |
position_ids = position_ids[:, -input_ids.shape[1] :] | |
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step | |
if inputs_embeds is not None and cache_position[0] == 0: | |
model_inputs = {"inputs_embeds": inputs_embeds} | |
else: | |
model_inputs = {"input_ids": input_ids.contiguous()} # `contiguous()` needed for compilation use cases | |
if cache_position[0] == 0: | |
# If we're in cached decoding stage, pixel values should be `None` because input ids do not contain special image token anymore | |
# Otherwise we need pixel values to be passed to model | |
model_inputs["pixel_values"] = pixel_values | |
model_inputs.update( | |
{ | |
"position_ids": position_ids, | |
"cache_position": cache_position, | |
"past_key_values": past_key_values, | |
"use_cache": use_cache, | |
"attention_mask": attention_mask, | |
} | |
) | |
return model_inputs | |